Hostname: page-component-cd9895bd7-dzt6s Total loading time: 0 Render date: 2024-12-23T13:28:35.116Z Has data issue: false hasContentIssue false

Diffusion-Scale Tightness of Invariant Distributions of a Large-Scale Flexible Service System

Published online by Cambridge University Press:  04 January 2016

A. L. Stolyar*
Affiliation:
Bell Laboratories
*
Current address: Lehigh University, Mohler Laboratory, 200 West Packer Avenue, Bethlehem, PA 18015, USA. Email address: [email protected]
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

A large-scale service system with multiple customer classes and multiple server pools is considered, with the mean service time depending both on the customer class and server pool. The allowed activities (routeing choices) form a tree (in the graph with vertices being both customer classes and server pools). We study the behavior of the system under a leaf activity priority (LAP) policy, introduced by Stolyar and Yudovina (2012). An asymptotic regime is considered, where the arrival rate of customers and number of servers in each pool tend to ∞ in proportion to a scaling parameter r, while the overall system load remains strictly subcritical. We prove tightness of diffusion-scaled (centered at the equilibrium point and scaled down by r−1/2) invariant distributions. As a consequence, we obtain a limit interchange result: the limit of diffusion-scaled invariant distributions is equal to the invariant distribution of the limiting diffusion process.

Type
General Applied Probability
Copyright
© Applied Probability Trust 

References

Aksin, Z., Armony, M. and Mehrotra, V. (2007). The modern call center: a multi-disciplinary perspective on operations management research. Production Operat. Manag. 16, 655688.CrossRefGoogle Scholar
Atar, R., Shaki, Y. Y. and Shwartz, A. (2011). A blind policy for equalizing cumulative idleness. Queueing Systems 67, 275293.CrossRefGoogle Scholar
Csörgő, M. and Horváth, L. (1993). Weighted Approximations in Probability and Statistics. John Wiley, Chichester.Google Scholar
Ethier, S. N. and Kurtz, T. G. (1986). Markov Processes. Characterization and Convergence. John Wiley, Chichester.CrossRefGoogle Scholar
Gamarnik, D. and Goldberg, D. A. (2013). Steady-state GI/GI/n queue in the Halfin-Whitt regime. Ann. Appl. Prob. 23, 23822419.Google Scholar
Gamarnik, D. and Momčilović, P. (2008). Steady-state analysis of a multiserver queue in the Halfin-Whitt regime. Adv. Appl. Prob. 40, 548577.CrossRefGoogle Scholar
Gamarnik, D. and Stolyar, A. L. (2012). Multiclass multiserver queueing system in the Halfin-Whitt heavy traffic regime: asymptotics of the stationary distribution. Queueing Systems 71, 2551.CrossRefGoogle Scholar
Gans, N., Koole, G. and Mandelbaum, A. (2003). Telephone call centers: tutorial, review, and research prospects. Manufacturing Service Operat. Manag. 5, 79141.CrossRefGoogle Scholar
Gurvich, I. and Whitt, W. (2009). Queue-and-idleness-ratio controls in many-server service systems. Math. Operat. Res. 34, 363396.CrossRefGoogle Scholar
Karatzas, I. and Shreve, S. E. (1991). Brownian Motion and Stochastic Calculus, 2nd edn. Springer, New York.Google Scholar
Liptser, R. Sh. and Shiryayev, A. N. (1989). Theory of Martingales. Kluwer, Dordrecht.CrossRefGoogle Scholar
Pang, G., Talreja, R. and Whitt, W. (2007). Martingale proofs of many-server heavy-traffic limits for Markovian queues. Prob. Surveys 4, 193267.CrossRefGoogle Scholar
Shakkottai, S. and Stolyar, A. L. (2002). Scheduling for multiple flows sharing a time-varying channel: the exponential rule. In Analytic Methods in Applied Probability (Amer. Math. Soc. Trans. Ser. 2 207), American Mathematical Society, Providence, RI, pp. 185201.Google Scholar
Stolyar, A. L. and Tezcan, T. (2010). Control of systems with flexible multi-server pools: a shadow routing approach. Queueing Systems 66, 151.CrossRefGoogle Scholar
Stolyar, A. L. and Tezcan, T. (2011). Shadow-routing based control of flexible multiserver pools in overload. Operat. Res. 59, 14271444.CrossRefGoogle Scholar
Stolyar, A. L. and Yudovina, E. (2012). Tightness of invariant distributions of a large-scale flexible service system under a priority discipline. Stoch. Systems 2, 381408.CrossRefGoogle Scholar
Stolyar, A. L. and Yudovina, E. (2013). Systems with large flexible server pools: instability of ‘natural’ load balancing. Ann. Appl. Prob. 23, 20992138.CrossRefGoogle Scholar
Ward, A. R. and Armony, M. (2013). Blind fair routing in large-scale service systems with heterogeneous customers and servers. Operat. Res. 61, 228243.CrossRefGoogle Scholar